As computing devices such as smartphones, smart televisions, and tablets have become the preferred way for many consumers to interact with digital content like movies and music, content providers have become interested in generating content recommendations that are tailored to each particular user's interests and preferences. A traditional way of generating such recommendations are through the use of recommender systems, that analyze user behavior to identify similarities between users and/or content in order to select content that is likely to be of interest to certain users.
Such recommender systems typically rely on techniques like matrix factorization, in which content objects and users are each characterized into vectors based upon content ratings, and then similarity between vectors is calculated. High similarity suggests a content item that is more likely to be of interest to a user. An exemplary matrix factorization technique is described in Koren et al., “Matrix Factorization Techniques for Recommender Systems,” Computer, IEEE Computer Society, pp. 42-49, August 2009.
However, computational techniques like matrix factorization suffer from significant drawbacks. For example, matrix factorization requires a large number of input parameters—namely, a feature vector for each user and each item—which necessitates a high amount of computational power in order to generate the recommendations in a computationally-effective (and cost-effective) way.
Other machine learning techniques like blind regression have similar deficiencies, such as the effectiveness of the Gaussian kernel used and the spasticity of the neighbor matrix used. One example of blind regression is described in Lee et al., “Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering,” 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. However, such techniques typically rely on a hand-picked Gaussian kernel that may not be optimized for the particular application. As shown in experiments, the performance of blind regression varies depending upon different datasets used. For a larger dataset, the overall performance of the blind regression technique is far from the benchmark (e.g., state-of-the-art matrix factorization algorithm). More specifically, the accuracy and response time of this algorithm depends on the density of the neighbor matrix for each case. For cases with a dense neighbor matrix, the computational processing time is longer and this could cause delay in recommendation service response. For cases with a sparse neighbor matrix, the algorithm tries to improve the accuracy via adjusting the overlapping threshold parameter (β). A higher β increases the accuracy, but reduces the density of the neighbor matrix. This could lead to an empty neighbor matrix and the algorithm would then fail to give an output—leaving the system with no recommendation, and thus no content, to provide to the user.